While humans are extremely good at learning new things quickly from seeing a couple of examples, AI systems often require a large amount of training data to adapt. Many scientists believe that the ability of making good credit assignment is among the most important that contributes to human's extremely efficient learning capability. As pointed out by [1]}, to gain more from each experience (e.g., a logged search session), it is not wise to remember too many details, but only those aspects that were relevant to the (user's search) goals. Learning could be far more efficient if we assign the credit for the success or failure not only to the final action that led to the result, but to earlier choices the DM made that selected the winning strategy.
